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Link to original content: https://unpaywall.org/10.1007/978-3-030-34995-0_68
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UAV Classification with Deep Learning Using Surveillance Radar Data

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Computer Vision Systems (ICVS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11754))

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Abstract

The Unmanned Aerial Vehicle (UAV) proliferation has raised many concerns, since their potentially malicious usage renders them as a detrimental tool for a number of illegal activities. Radar based counter-UAV applications provide a robust solution for UAV detection and classification. Most of the existing research addresses the problem of UAV classification by extracting features from the time variations of the Fourier spectra. Yet, these solutions require that the UAV is illuminated by the radar for a longer time which can be only met by a tracking radar architecture. On the other hand, surveillance radar architectures don’t have such a cumbersome requirement and are generally superior in maintaining situational awareness, due their ability for constantly searching on a 360\(^{\circ }\) area for targets. Nevertheless, the available automatic UAV classification methods for this type of radar sensors are relatively inefficient. This work proposes the incorporation of the deep learning paradigm in the classification pipeline, to provide an alternative UAV classification method that can handle data from a surveillance radar. Therefore, a Deep Neural Network (DNN) model is employed to discern between UAVs and negative examples (e.g. birds, noise, etc.). The conducted experiments demonstrate the validity of the proposed method, where the overall classification accuracy can reach up to \(95.0\%\).

This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement N\(^{\circ }\) 740859, ALADDIN.

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Acknowledgments

Special thanks to IDS Ingegneria Dei Sistemi S.p.A. for providing their radar sensor, the signal processing knowledge and the assistance in the dataset creation.

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Correspondence to Stamatios Samaras .

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Samaras, S., Magoulianitis, V., Dimou, A., Zarpalas, D., Daras, P. (2019). UAV Classification with Deep Learning Using Surveillance Radar Data. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_68

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  • DOI: https://doi.org/10.1007/978-3-030-34995-0_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34994-3

  • Online ISBN: 978-3-030-34995-0

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